The capability to accurately price products improves a retail organization's ability to maximize profit, limit unprofitable product substitution, and take advantage of potential cross-sell opportunities is a desirable objective for a retail organization. Thus, business tools that provide a retailer with the capability to accurately and reliably price products on a routine basis, and automatically adjust pricing in response to new information, whether that information is internal sales and promotion information or external competition information are greatly desired.
Teradata, a division of NCR Corporation, has developed an analytical application, referred to as Teradata Price Optimizer (PO), which determines the best price across a set of a retailer's products. On-demand, it automatically creates statistical models, and without any user intervention, identifies and estimates product cross-sell and substitution effects. It does all modeling and analysis directly on the data in a Teradata data warehouse system, without any data extraction or manual data preparation. Output from the Teradata Price Optimizer analytic application can be used operationally, to set new product prices. Additionally, the Teradata Price Optimizer application serves as a decision support tool to help retailers understand the influencers on their product sales and profitability.
The application is designed to do the following:                Automatically create pricing models. Statistical models are automatically created using relevant information in the database. These models estimate price elasticity given the impact of price and other effects, such as promotions.        FIG. 5 provides a chart illustrating the effect of product price 505 changes on product sales volume 507 and profit 509. Graph 511 shows how sales volume increases linearly as product price decreases. Graph 513 illustrates how profit first increases, then decreases as product price decreases.        Using market basket data, automatically identify all product cross-sell and substitution effects and combine these in the analysis. There is no requirement for users to identify cross sell products or substitute products. The system does this by analyzing market basket data, taking into account product availability.        The chart shown in FIG. 6 illustrates the positive effect on profit 605 associated with products C through V that results from a decrease in the sales price of a related product B. Other products, not shown, will experience a decrease in sales and profit as the price of product B decreases.        Combine a product's elasticity with cross sell and cannibalization effects to understand how these other factors influence the decision to change product prices.        Results of a products' price elasticity, plus all cross-sell and cannibalization effects resulting from changes in the products' price are illustrated in the chart shown in FIG. 7. Graph 707 shows profit increasing as price decreases, based on the price elasticity of Product C. Graph 709 shows profits increasing, then decreasing, taking into consideration the elasticity of Product C, plus all cross-sell and cannibalization effects.        Optimize product prices across products, taking into consideration cross-sell and substitution effects as well as a products' own elasticity.        Results of a products' price elasticity, plus all cross-sell and cannibalization effects resulting from changes in the products' price are illustrated in the chart shown in FIG. 8. The chart illustrated in FIG. 8 shows cross-sell and cannibalization effects at each percentage change for exemplary Product D. The bars in the center of the graph, identified by reference numeral 807, show how profit generally increases as the price of Product D decreases. To the right of the graph, bars 809 and 811, located above bars 807, illustrate profit increases associated with cross-sell products. Also to the right of the graph, but below the 0 (zero) profit line, bars 813 through 819, illustrate the negative profit effects due to cannibalization products.        Perform pricing analyses separately for each store within a retailer organization. Pricing can be performed for a user-defined group of stores, or all stores.        Perform pricing analyses at the lowest level of a product hierarchy. Individual analysis can be performed for each product.        Allow users to perform a ‘what if’ analysis to determine the impact of pricing a product differently than what is recommended. The ‘what if’ analysis also takes into consideration cross sell and substitution effects. There is often a business consideration that will override a price change, the system allows this, but also provides the user with the information to understand the business impact of this decision.        Recommend to users the products that represent the best opportunities and lowest risk for making pricing changes. Assessment of opportunity and risk is based on business factors relevant to the companies business. Weights on each factor are set by the user and can be adjusted to automatically understand the impact of business assumptions on product pricing opportunity and risk.        FIG. 9 illustrates the opportunity matrix chart 901 showing a plot of opportunity scores 903 vs. ability to change scores 905 for numerous products represented by points displayed in the chart. The chart is divided into four quadrants, where the most desirable products to perform elasticity are displayed in the upper right quadrant.        Create new pricing models on-demand. This is done by the retailer, with a few mouse clicks and input.        